RNAseq of diverse spring wheat cultivars released during last 110 years

Here, we performed RNA-seq based expression analysis of root and leaf tissues of a set of 24 historical spring wheat cultivars representing 110 years of temporal genetic variations. This huge 130 tissues RNAseq dataset was initially used to study expression pattern of 97 genes regulating root growth and development in wheat. Root system architecture (RSA) is an important target for breeding stress-resilient and high-yielding wheat cultivars under climatic fluctuations. However, root transcriptome analysis is usually obscured due to challenges in root research due to their below ground presence. We also validated the dataset by performing correlation analysis between expression of RSA related genes in roots and leaves with 25 root traits analyzed under varying moisture conditions and 10 yield-related traits. The Pearson’s correlation coefficients between root phenotypes and expression of root-specific genes varied from −0.72 to 0.78, and strong correlations with genes such as DRO1, TaMOR, ARF4, PIN1 was observed. The presented datasets have multiple uses such as a) studying the change in expression pattern of genes during time, b) differential expression of genes in two very important tissues of wheat i.e., leaf and roots, and c) studying customized expression of genes associated with important phenotypes in diverse wheat cultivars. The initial findings presented here provided key insights into understanding the transcriptomic basis of phenotypic variability of RSA in wheat cultivars.


Background & Summary
Bread wheat (Triticum aestivum) is one of the most important staple food crops providing 55% of carbohydrates to the world population.The grain yield of wheat has to increase at an average annual rate of ~2% in a limited area of cultivated land to meet the world food demand 1 .A deeper understanding of wheat genetics is required to address the primary challenge of sustaining food security in the context of climate change to feed the growing population.It is critical to deepen the knowledge of the wheat genomics and its genetic composition as well as the broad range of sequencing and transcriptomics data to understand genetic basis of wheat adaptability to target environments 2 .Identification and functional characterization of genes that regulate developmental stages critical for withstanding climatic fluctuations is an important aspect of this area of research.Similarly, it is central to functional genetic studies to analyze dynamic expression patterns of each gene contributing to plant development in various tissues and response to various environmental stimuli 3 .
Roots are significant for the production of food grains such as wheat and rice 4 .A variety of morphological and physiological traits expressed by root systems facilitate the uptake of water and nutrients.Similar to above-ground traits, there must be an understanding of unique root system architecture (RSA) for optimum resource acquisition 5 .Since roots are important components of breeding programs, it is crucial to understand the molecular mechanisms involved in root formation especially under challenging conditions.
In order to pinpoint the genetic components influencing the root growth in maize, rice and other crops, a variety of forward and reverse genetics techniques including transcriptomics and functional genomics have been applied 6 .The transcriptome studies using next-generation sequencing (NGS) technologies have paved the way in linking genotype to phenotype and can detect the molecular mechanisms underlying plant responses to abiotic stress 7 .Presently, several population-wide transcriptome analyses have been conducted in cereal crops including rice 8 , wheat 9 , and barley 10 .These studies unravelled the associations between gene expression and traits; however, field studies have generally been restricted to transcriptomics of above-ground shoots due to the challenge of sampling root tissues in field conditions.RNA-seq previously known as whole transcriptome shotgun sequencing has excitingly shaped whole transcriptome profiling 7 .It can identify transcript levels, expressed polymorphisms, and splicing isoforms.The development of high-throughput next-generation RNA-seq technologies provides new insights into transcriptome analysis such as a detailed expression profile, higher sensitivity to genes expressing at both high and low extremes, and no limitation by the lack of prior genome knowledge 11 .RNA-seq studies in wheat are increasing rapidly owing to the reconstruction of the entire transcriptome using the short paired-end (PE) assembly of de novo reads 12 and provide a precise measurement of transcript levels.In wheat, some large-scale RNAseq studies available where transcriptome of multiple tissues from a single cultivar are reported like in Chinese Spring and Azhurnaya 13 .In this study, we conducted transcriptome profiling using RNA-seq on a set of 24 bread wheat varieties with diverse phenotypes supported by their large-scale phenotypic variation in agronomic and RSA traits 14,15 .We initially analyzed the dataset to identify expression variation of potential transcripts or genes involved in RSA and validated by correlation analysis with RSA phenotypes.

Methods
Plant material.A panel of 24 historical spring wheat cultivars released in Pakistan was selected for this study.
The cultivar name, year of release, and pedigree are given in Table 1.These cultivars selected based on the year of release to represent the cultivated diversity over the course of 110 years.
Growth and RNA isolation.The seeds of 24 wheat cultivars were surface sterilized using 3% NaOCl and were sown in triplicates in plastic trays containing peat moss.Two weeks after germination (at Zadoks stage 2), seedling leaf and root tissues were collected and subjected to total RNA extraction.RNA extraction was performed using EasyPure Plant RNA Kit (ER301-01) following the instructions provided by manufacturer and quantified using Nanodrop 2000 spectrophotometer (Thermo Fisher Scientific, USA).RNA Sequencing and identification of differentially expressed genes.The RNA samples were sequenced from Beijing Genomics Institute (BGI), China.For cDNA synthesis, the oligo (dT) method was used.The 50-bp single-end sequencing libraries were constructed, and BGISEQ-500 platform was used for sequencing using standard protocols.'Clean data' was produced as FastQ data files using SOAPnuke version 2.1.6.Mapping with reference genome of bread wheat 16 was done using HISAT2 software v 2.2.1 17 .Bowtie software was used for alignment of reference sequence with reads 18 .The reads were then quantified using featureCounts software and differentially expressed genes (DEGs) were identified using DeSEQ. 2 in R v 4.1.1.The threshold value for filtering of DEGs was set at 0.1.All the DEG files were then culminated into a single file used for further analysis 19 .The R codes were used to generate heatmaps directly from the normalized count file 20 , or phenotypic data from the diversity panel was used to calculate correlation values and plot correlation values as heatmaps 20 .
Phenotyping for agronomic traits and root system architecture.The agronomic traits of the diversity panel were taken from our previous experiment 21 .Briefly, the diversity panel was planted at five locations and important agronomic traits were recorded.The phenotyping for RSA architecture traits has been described in detail 14 .The imaging platform consisting of RhizoVision crown hardware 22 controlled by RhizoVision Imager software was used for root image acquisition and details have been described previously 14 .The RSA traits included in the study were maximum weight (MaxW), maximum diameter (MaxD), lower root area (LRA), median number of roots (MNR), steep angle frequency (StAF), solidity (S), volume diameter (VD), surface area (SA), network area (NtA), projected area diameter (PAD), surface area diameter (SAD), median angle frequency (MAF), average root orientation (ARO), shallow angle frequency (SAF), depth (D), width to depth ratio (WDR), maximum number of roots (MaxNR), number of root tips (NRT), volume (V), perimeter (P), total root length (TRL), root length diameter (RLD), convex area (CA), average diameter (AD), and median diameter (MD).The correlation between gene expression and various traits including RSA traits, root hair length and density under low and high phosphorous treatments, and yield-related traits was determined using 'psych' package in R version 4.2.1.Fig. 4 Heatmap showing significant correlations between expression of RSA-related genes in roots, yieldrelated traits, and root traits under control and drought stress conditions.The size of the circle explains the extent of correlation.Traits are abbreviated as; Maximum weight (MaxW), Maximum diameter (MaxD), Lower root area (LRA), median number of roots (MNR), steep angle frequency (StAF), solidity (S), volume diameter (VD), surface area (SA), network area (NtA), projected area diameter (PAD), surface area diameter (SAD), median angle frequency (MAF), average root orientation (ARO), shallow angle frequency (SAF), depth (D), width to depth ratio (WDR), maximum number of roots (MaxNR), number of root tips (NRT), volume (V), perimeter (P), total root length (TRL), root length diameter (RLD), convex area (CA), average diameter (AD), median diameter (MD), spikes per spike (SpPS), plant height (PH), tillers per plant (TPP), grain yield (GY), grain length (GL), thousand kernel weight (TKW), grain density (GD), grains per spike (GPS), spike length (SL), and grain weight (GW).

Fig. 1 Fig. 2
Fig. 1 Differential expression of RSA-related genes in (A) leaf and (B) root tissues of 24 bread wheat cultivars.The original gene names have been used while Traes IDs can be found in the associated excel file available at FigShare under https://doi.org/10.6084/m9.figshare.23292389.

Table 1 .
List of historical spring wheat cultivars with release year and pedigree.